Spatial Heterogeneity in Clean Cooking Adoption in Kathmandu Valley, Evidence from Multiscale Geographically Weighted Regression

Hari Prasad Ghimire; Prem Bahadur Giri

doi:10.18535/sshj.v9i11.2107

Abstract

This study quantifies ward-level differences in clean cooking adoption in Kathmandu Valley and pinpoints where access, reliability, and socioeconomic factors matter most. We analyze a household survey of 384 households aggregated to 71 wards. The outcome is the primary use of clean fuels, LPG, electric, or biogas. Key predictors are traveling time to the nearest LPG outlet, weekly electricity outage hours, income band, and education of the household head. Methods include descriptive ward statistics, spatial patterning, and a global OLS with heteroscedasticity robust errors. MGWR is planned to recover spatially varying effects across wards. Mean ward adoption is 31.5 %, ranging from 0 to 100 %. Adoption is lower where LPG travel time is longer, r equals −0.57, and where outages are higher, r equals −0.58. In the global model, each additional minute to LPG reduces the probability of clean adoption by 1.5 % points; the coefficient equals −0.015, t equals −4.21. Each extra outage hour per week reduces adoption by 1.67 % points; the coefficient equals −0.0167, t equals −2.52. Income has a smaller positive average effect, coefficient equals 0.034, t equals 1.55. Education is positive but imprecise; the coefficient equals 0.026, t equals 1.50. Model fit is modest, R-squared equals 0.111 and adjusted R-squared equals 0.095, underscoring the limits of global averages. Inner wards with short travel times and fewer outages lead, while fringe wards lag. Results support targeted, ward-specific interventions.

Keywords

Clean cookingLPGElectric cookingBiogasMGWRKathmandu Valley

References

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Ramirez, C., Khavari, B., Oberholzer, A., Ghimire, B. R., Mishra, B., Sinclair-Lecaros, S., Mentis, D., Gurung, A., Khatiwada, D., & Nerini, F. F. (n.d.). Achieving Nepal’s clean cooking ambitions, an open source spatially explicit approach. SSRN Working Paper, 4726269. https://ssrn.com/abstract=4726269Google Scholar ↗
Regmi, S., & Adhikary, S. (2012). Solar energy potential in Kathmandu Valley, Nepal. Journal of Hydrology and Meteorology, 8(1), 77–82. https://doi.org/10.3126/jhm.v8i1.15576DOI ↗Google Scholar ↗
Rogers, B. (2020). Cooking fuel stacking, implications for willingness to switch to clean fuels in peri-urban Kathmandu Valley, Nepal. Unpublished manuscript.Google Scholar ↗
Roy, S., Majumder, S., Bose, A., & Roy Chowdhury, I. R. (2024). The rich-poor divide, unravelling the spatial complexities and determinants of wealth inequality in India. Applied Geography, 166, 103267. https://doi.org/10.1016/j.apgeog.2024.103267DOI ↗Google Scholar ↗
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Shen, Z., Chen, C.-F., Zhou, H., Fefferman, N., & Shrestha, S. (2023). Community vulnerability is the key determinant of diverse energy burdens in the United States. Energy Research and Social Science, 97, 102949. https://doi.org/10.1016/j.erss.2023.102949DOI ↗Google Scholar ↗
Singh, D. (2019). Linking household energy to local and global environmental change, understanding impacts of clean cooking interventions in rural India. Doctoral dissertation.Google Scholar ↗
Sun, Z.-Y., Zhu, X., Li, W., Liu, B., Li, Z., Wu, Y., & Zhu, J. (n.d.). Exploring the spatial heterogeneity of integrated green–grey infrastructure and carbon emissions in village units with the geographically weighted regression model. SSRN Working Paper, 5088203. https://ssrn.com/abstract=5088203Google Scholar ↗
Wang, F., & Wang, Y. (2024). How geographic accessibility and rural governance mitigate the impact of multiple risks on rural households’ well-being, evidence from the Dabie Mountains in China. Journal of Geographical Sciences, 34(6), 1195–1227. https://doi.org/10.1007/s11442-024-2183-3DOI ↗Google Scholar ↗
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[refR-1] Ahoudou, I., Fassinou Hotegni, N. V., Adjé, C. O., Akponikpè, T. L., Sogbohossou, D. E., Fanou Fogny, N., Assogba Komlan, F., Moumouni-Moussa, I., & Achigan-Dako, E. G. (2025). Evaluating logistic regression and geographically weighted logistic regression models for predicting orange-fleshed sweet potato adoption intention in Benin. Scientific Reports, 15(1), 8927.Google Scholar ↗

[refR-2] Bharadwaj, B., Malakar, Y., Herington, M., & Ashworth, P. (2022). Context matters, unpacking decision-making, external influences and spatial factors on clean cooking transitions in Nepal. Energy Research and Social Science, 85, 102408. https://doi.org/10.1016/j.erss.2021.102408DOI ↗Google Scholar ↗

[refR-3] Boutayeb, A., Lahsen-Cherif, I., & El Khadimi, A. (2025). When machine learning meets geospatial data, a comprehensive GeoAI review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 18, 13135–13191. https://doi.org/10.1109/JSTARS.2025.3568715DOI ↗Google Scholar ↗

[refR-4] Chen, R., Liao, C., & Ren, C. (2024). Navigating China’s path to a low-carbon economy, a comprehensive review of regional and provincial carbon emissions policies. Urban Climate, 55, 101959. https://doi.org/10.1016/j.uclim.2024.101959DOI ↗Google Scholar ↗

[refR-5] Cui, W., Li, J., Xu, W., & Güneralp, B. (2021). Industrial electricity consumption and economic growth, a spatio-temporal analysis across prefecture-level cities in China from 1999 to 2014. Energy, 222, 119932. https://doi.org/10.1016/j.energy.2021.119932DOI ↗Google Scholar ↗

[refR-6] Dong, Q., Huang, Z., Zhou, X., Guo, Y., Scheuer, B., & Liu, Y. (2023). How building and street morphology affect CO2 emissions, evidence from a spatially varying relationship analysis in Beijing. Building and Environment, 236, 110258. https://doi.org/10.1016/j.buildenv.2023.110258DOI ↗Google Scholar ↗

[refR-7] Feng, Y., Miao, Y., & Turner, E. (2025). Examining spatial variations in the relationship between domestic energy consumption and its driving factors using multiscale geographically weighted regression, a case study in Nottingham, England. Energy, Sustainability and Society, 15(1), 24. https://doi.org/10.1186/s13705-025-00523-1DOI ↗Google Scholar ↗

[refR-8] Fotheringham, A. S., Kao, C.-L., Yu, H., Bardin, S., Oshan, T., Li, Z., Sachdeva, M., & Luo, W. (2024). Exploring spatial context, a comprehensive bibliography of GWR and MGWR. arXiv Preprint, arXiv:2404.16209.Google Scholar ↗

[refR-9] Guo, Q., & Sun, H. (2025). Spatial distribution of Chinese urban residents’ electricity consumption and its driving factors, an empirical study based on the MGWR model. Energy, 319, 135076. https://doi.org/10.1016/j.energy.2025.135076DOI ↗Google Scholar ↗

[refR-10] He, X., Zhou, C., Wang, Y., & Yuan, X. (2021). Risk assessment and prediction of COVID-19 based on epidemiological data from spatiotemporal geography. Frontiers in Environmental Science, 9, 634156. https://doi.org/10.3389/fenvs.2021.634156DOI ↗Google Scholar ↗

[refR-11] Hlatshwayo, S. N., Tesfamichael, S. G., & Kganyago, M. (2024). Predicting tropospheric nitrogen dioxide column density in South African municipalities using socio-environmental variables and multiscale geographically weighted regression. PLOS ONE, 19(8), e0308484. https://doi.org/10.1371/journal.pone.0308484DOI ↗Google Scholar ↗

[refR-12] Jiao, Z., & Tao, R. (2025). Geographical graph attention networks, spatial deep learning models for spatial prediction and exploratory spatial data analysis. Transactions in GIS, 29(2). https://doi.org/10.1111/tgis.70029DOI ↗Google Scholar ↗

[refR-13] Jo, H., & Kim, H. (2022). Analyzing electricity consumption factors of buildings in Seoul, Korea using multiscale geographically weighted regression. Buildings, 12(5), 678. https://doi.org/10.3390/buildings12050678DOI ↗Google Scholar ↗

[refR-14] Khoun, T., Poortinga, A., Thwal, N. S., González de Alba, I., McMahon, A., & Mendez, C. (2025). Mapping the dimensions of poverty through big data, socioeconomic surveys and machine learning in Cambodia. Social Indicators Research,1–26. https://doi.org/10.1007/s11205-025-03421-2DOI ↗Google Scholar ↗

[refR-15] Kilian, L., Owen, A., Newing, A., & Ivanova, D. (2022). Exploring transport consumption-based emissions, spatial patterns, social factors, well-being, and policy implications. Sustainability, 14(19), 11844. https://doi.org/10.3390/su141911844DOI ↗Google Scholar ↗

[refR-16] Lei, H., Zeng, S., Namaiti, A., & Zeng, J. (2023). The impacts of road traffic on urban carbon emissions and the corresponding planning strategies. Land, 12(4), 800. https://doi.org/10.3390/land12040800DOI ↗Google Scholar ↗

[refR-17] Li, Z., Wang, F., Kang, T., Wang, C., Chen, X., Miao, Z., Zhang, L., Ye, Y., & Zhang, H. (2022). Exploring differentiated impacts of socioeconomic factors and urban forms on city-level CO2 emissions in China, spatial heterogeneity and varying importance levels. Sustainable Cities and Society, 84, 104028. https://doi.org/10.1016/j.scs.2022.104028DOI ↗Google Scholar ↗

[refR-18] Liu, C., Chen, G., Li, H., Li, J., & Muga, G. (2025). Impact of urban morphology on carbon emission differentiation at county scale in China. Land, 14(6), 1163. https://doi.org/10.3390/land14061163DOI ↗Google Scholar ↗

[refR-19] Liu, Q., Lu, B., Lin, W., Li, J., Lu, Y., & Duan, Y. (2025). An improved quantitative analysis method for the unequal supply and demand of ecosystem services and hierarchical governance suggestions. Land, 14(3), 528. https://doi.org/10.3390/land14030528DOI ↗Google Scholar ↗

[refR-20] Liu, W., Guo, C., Gui, X., Li, D., & Chen, X. (2025). Exploring preferences for household energy technology adoption from a spatiotemporal perspective, evidence from demographics, the economy, and the environment. Energy, 319, 134957. https://doi.org/10.1016/j.energy.2025.134957DOI ↗Google Scholar ↗

[refR-21] Liu, W., Xue, Y., & Shang, C. (2023). Spatial distribution analysis and driving factors of traditional villages in Henan province, a comprehensive approach via geospatial techniques and statistical models. Heritage Science, 11(1), 108. https://doi.org/10.1186/s40494-023-01038-8DOI ↗Google Scholar ↗

[refR-22] Moore, D., & Webb, A. L. (2022). Evaluating energy burden at the urban scale, a spatial regression approach in Cincinnati, Ohio. Energy Policy, 160, 112651. https://doi.org/10.1016/j.enpol.2021.112651DOI ↗Google Scholar ↗

[refR-23] Oshan, T. M., Smith, J. P., & Fotheringham, A. S. (2020). Targeting the spatial context of obesity determinants via multiscale geographically weighted regression. International Journal of Health Geographics, 19(1), 11. https://doi.org/10.1186/s12942-020-00203-6DOI ↗Google Scholar ↗

[refR-24] Ramirez, C., Khavari, B., Oberholzer, A., Ghimire, B. R., Mishra, B., Sinclair-Lecaros, S., Mentis, D., Gurung, A., Khatiwada, D., & Nerini, F. F. (2024). Achieving Nepal’s clean cooking ambitions, an open source and geospatial cost–benefit analysis. The Lancet Planetary Health, 8(10), e754–e765. https://doi.org/10.1016/S2542-5196(24)00205-0DOI ↗Google Scholar ↗

[refR-25] Ramirez, C., Khavari, B., Oberholzer, A., Ghimire, B. R., Mishra, B., Sinclair-Lecaros, S., Mentis, D., Gurung, A., Khatiwada, D., & Nerini, F. F. (n.d.). Achieving Nepal’s clean cooking ambitions, an open source spatially explicit approach. SSRN Working Paper, 4726269. https://ssrn.com/abstract=4726269Google Scholar ↗

[refR-26] Regmi, S., & Adhikary, S. (2012). Solar energy potential in Kathmandu Valley, Nepal. Journal of Hydrology and Meteorology, 8(1), 77–82. https://doi.org/10.3126/jhm.v8i1.15576DOI ↗Google Scholar ↗

[refR-27] Rogers, B. (2020). Cooking fuel stacking, implications for willingness to switch to clean fuels in peri-urban Kathmandu Valley, Nepal. Unpublished manuscript.Google Scholar ↗

[refR-28] Roy, S., Majumder, S., Bose, A., & Roy Chowdhury, I. R. (2024). The rich-poor divide, unravelling the spatial complexities and determinants of wealth inequality in India. Applied Geography, 166, 103267. https://doi.org/10.1016/j.apgeog.2024.103267DOI ↗Google Scholar ↗

[refR-29] Shen, S.-L., Yan, W.-L., & Cui, J.-L. (2024). Testing spatial heteroscedasticity in mixed geographically weighted regression models. Journal of Statistical Computation and Simulation, 94(15), 3409–3426. https://doi.org/10.1080/00949655.2024.2386115DOI ↗Google Scholar ↗

[refR-30] Shen, Z., Chen, C.-F., Zhou, H., Fefferman, N., & Shrestha, S. (2023). Community vulnerability is the key determinant of diverse energy burdens in the United States. Energy Research and Social Science, 97, 102949. https://doi.org/10.1016/j.erss.2023.102949DOI ↗Google Scholar ↗

[refR-31] Singh, D. (2019). Linking household energy to local and global environmental change, understanding impacts of clean cooking interventions in rural India. Doctoral dissertation.Google Scholar ↗

[refR-32] Sun, Z.-Y., Zhu, X., Li, W., Liu, B., Li, Z., Wu, Y., & Zhu, J. (n.d.). Exploring the spatial heterogeneity of integrated green–grey infrastructure and carbon emissions in village units with the geographically weighted regression model. SSRN Working Paper, 5088203. https://ssrn.com/abstract=5088203Google Scholar ↗

[refR-33] Wang, F., & Wang, Y. (2024). How geographic accessibility and rural governance mitigate the impact of multiple risks on rural households’ well-being, evidence from the Dabie Mountains in China. Journal of Geographical Sciences, 34(6), 1195–1227. https://doi.org/10.1007/s11442-024-2183-3DOI ↗Google Scholar ↗

[refR-34] Wang, L., Ba, Z., & Wang, Y. (2025). How does adoption behavior towards ICT policies affect digital divides, evidence from Chinese prefecture-level cities. Technology in Society, 81, 102803. https://doi.org/10.1016/j.techsoc.2024.102803DOI ↗Google Scholar ↗

[refR-35] Xie, C., Yu, D., Ciyun, C., Zheng, X., & Peng, B. (2022). Exploring the spatiotemporal impacts of the built environment on taxi ridership using multisource data. Sustainability, 14(10), 6045. https://doi.org/10.3390/su14106045DOI ↗Google Scholar ↗

[refR-36] Zeng, C., Yang, L., Zhu, A.-X., Rossiter, D. G., Liu, J., Liu, J., Qin, C., & Wang, D. (2016). Mapping soil organic matter concentration at different scales using a mixed geographically weighted regression method. Geoderma, 281, 69–82. https://doi.org/10.1016/j.geoderma.2016.06.033DOI ↗Google Scholar ↗

[refR-37] Zhang, B., Liu, Y., Liu, Y., & Lyu, S. (2024). Spatiotemporal evolution and influencing factors for urban resilience in China, a provincial analysis. Buildings, 14(2),502. https://doi.org/10.3390/buildings14020502DOI ↗Google Scholar ↗

[refR-38] Zhang, Z., Zhang, J., Liu, L., Gong, J., Li, J., & Kang, L. (2023). Spatial–temporal heterogeneity of urbanization and ecosystem services in the Yellow River Basin. Sustainability, 15(4), 3113. https://doi.org/10.3390/su15043113DOI ↗Google Scholar ↗

[refR-39] Zhong, Q., Fu, H., Yan, J., & Li, Z. (2024). How does energy utilization affect rural sustainability development in traditional villages, re-examination from the coupling coordination degree of atmosphere-ecology-socioeconomics system. Building and Environment, 257, 111541. https://doi.org/10.1016/j.buildenv.2024.111541DOI ↗Google Scholar ↗

[refR-40] Zhou, K., Zhou, F., Chen, L., Li, H., Wang, H., & Li, J. (2024). Alleviating environmental pressure from livestock production, a livestock-resource environmental carrying capacity perspective in China. Ecological Indicators, 160, 111800. https://doi.org/10.1016/j.ecolind.2024.111800DOI ↗Google Scholar ↗

[refR-41] Zhou, Y., Zeng, Y., & Qian, C. (2025). Research on the configuration of commercial facilities around universities based on the characteristics and satisfaction of university students’ consumption behavior. Journal of Asian Architecture and Building Engineering. https://doi.org/10.1080/13467581.2025.2483448DOI ↗Google Scholar ↗